Background: Some patients with low-grade glioma have extraordinarily long survival times; current, early treatment does not prolong their lives. For this reason, therapies that sometimes have neurologic side effects are often deferred intentionally. Methods: In a study of oligodendrogliomas, we used a quantitative method of MR analysis based on the S-transform to investigate whether codeletion of chromosomes1p and19q, a marker of good prognosis, could be predicted accurately by measuring image texture. Results: Differences in texture were seen between tumors with codeletion of chromosomes 1p and 19q and those with intact 1p and 19q alleles on contrast-enhanced T1-weighted and T2-weighted MR images. Quantitative MR texture onT2 images predicted codeletion of chromosomes 1p and 19q with high sensitivity and specificity. Conclusions: This new method of MR image interpretation may have the potential to augment the diagnostic assessment of patients with suspected low-grade glioma.
Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.
Abstract. In glioblastoma (GBM), promoter methylation of the DNA repair gene MGMT is associated with benefit from chemotherapy. Because MGMT promoter methylation status can not be determined in all cases, a surrogate for the methylation status would be a useful clinical tool. Correlation between methylation status and magnetic resonance imaging features has been reported suggesting that non-invasive MGMT promoter methylation status detection is possible. In this work, a retrospective analysis of T2, FLAIR and T1-post contrast MR images in patients with newly diagnosed GBM is performed using L1-regularized neural networks. Tumor texture, assessed quantitatively was utilized for predicting the MGMT promoter methylation status of a GBM in 59 patients. The texture features were extracted using a space-frequency texture analysis based on the S-transform and utilized by a neural network to predict the methylation status of a GBM. Blinded classification of MGMT promoter methylation status reached an average accuracy of 87.7%, indicating that the proposed technique is accurate enough for clinical use.
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